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Joint analysis of regulatory networks and expression
profilesRon Shamir
School of Computer ScienceTel Aviv University
April 2013
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Sources: Igor Ulitsky and Ron Shamir. Identification of Functional Modules using Network Topology and High-Throughput Data. BMC Systems Biology 1:8 (2007). Igor Ulitsky and Ron Shamir. Identifying functional modules using expression profiles and confidence-scored protein interactions. Bioinformatics Vol. 25 no. 9 1158-1164 (2009) .
Outline• Background• Joint network and expression profiles
– Matisse– Cezanne
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DNA RNA protein
transcription translation
The hard disk
One program
Its output
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DNA Microarrays / RNA-seq
• Simultaneous measurement of expression levels of all genes / transcripts.
• Perform 105-109 measurements in one experiment
• Allow global view of cellular processes. • The most important biotechnological
breakthroughs of the last /current decade
http://www.biomedcentral.com/1471-2105/12/323/figure/F25
The Raw Data
genes
experiments
Entries of the Raw Data matrix: expression levels.Ratios/absolute values/…
• expression pattern for each gene• Profile for each experiment/condition/sample/chip
Needs normalization!
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EXPression ANalyzer and DisplayER
Clustering Identify clusters of co-expressed
genes
CLICK, KMeans, SOM, hierarchical
http://acgt.cs.tau.ac.il/expander
A. Maron, R. Sharan Bioinformatics 03
Function.
enrichment
GO, TANGO
Visualization
Promoter analysis
Analyze TF binding sites of
co-regulated genes
PRIMA
Biclustering Identify
homogeneous submatrices
SAMBA
A. Maron-Katz, A. Tanay, C. Linhart, I. Steinfeld, R. Sharan, Y. Shiloh, R. Elkon BMC Bioinformatics 05
microRNA
function
inference: FAME
Ulitsky et al. Nature Protocols 10
Networks of Protein-protein interactions (PPIs)
• Large, readily available resource• Representation: Network with
nodes=proteins/genes edges=interactions
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Analysis methods:Global propertiesMotif content analysisComplex extractionCross-species comparison
• Potential inroad into pathways and function
• Can the network help to improve the analysis?
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Goal
• Challenge: Detect active functional modules: connected subnetwork of proteins whose genes are co-expressed
• “Where is the action in the network in a particular experiment?”
• Input: Expression data and a PPI network
• Output: a collection of modules– Connected PPI subnetworks– Correlated expression profiles
Interaction
High expression similarity
http://acgt.cs.tau.ac.il/matisse16
Modular Analysis for Topology of Interactions and
Similarity SEts
Probabilistic model• Event Mij: i,j are mates = highly co-expressed
• P(Sij|Mij) ~ N(m , 2m)
• P(Sij|Mij) ~ N(n , 2n)
• H0: U is a set of unrelated genes• H1: U is a module = connected subnetwork with high internal similarity
• Ri: gene i transcriptionally regulated• m: fraction of mates out of module gene pairs that are transcriptionally regulated
• m= P(Mij| Ri Rj, H1)• pm: fraction of mates out of all gene pairs that are transcriptionally regulated
))P(R(R)|HP(S jiMUxU 17
Probabilistic model (2)• Is connected gene set U a module?
Assuming pair indep:• Define m
ij= m P(Ri)P(Rj)
• Define nij= pm P(Ri)P(Rj).
• Likelihood ratio Pr(Data|H1)/Pr Data|H0)
• Taking log: sum of terms ij:
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))P(R(R)|HP(S jiMUxU
Probabilistic model - summary
• Similarities: mixture of two Gaussians• For a candidate group U, the likelihood ratio of originating from a module or from the background is
• Module score = Gene group likelihood ratio = sum over all the gene pairs
• Find connected subgraphs U with high WU
( , ) ( , )
( | )( | )log log
( | ) ( | )ij MU U M
U iji j U U i j U UU U N ij N
P S HP S HW w
P S H P S H
))P(R(R)|HP(S jiMUxU 19
Complexity
• Finding heaviest connected subgraph: NP hard even without connectivity constraints (+/- edge weights)
• Devised a heuristic for the problem
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MATISSE workflow
• Seed generation• Greedy optimization• Significance filtering
Finding seeds
• Three seeding alternatives tested• All alternatives build a seed and
delete it from the network• Building small seeds around single
nodes:• Best neighbors• All neighbors
• Approximating the heaviest subgraph• Delete low-degree nodes and record the
heaviest subnetwork found
Greedy optimization
• Simultaneous optimization of all the seeds
• The following steps are considered:• Node addition• Node removal• Assignment change• Module merge
Front vs. Back nodes
• Only a fraction of the genes (front nodes) have meaningful similarity values
• MATISSE can link them using other genes (back nodes).
• Back nodes correspond to:– Unmeasured transcripts– Post-translational regulation– Partially regulated pathways 24
Test case: Yeast osmotic shock
• Network: 65,990 PPIs & protein-DNA interactions among 6,246 genes
• Expression: 133 experimental conditions – response of perturbed strains to osmotic shock (O’Rourke & Herskowitz 04)
• Front nodes: 2,000 genes with the highest variance
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Performance comparison
0
10
20
30
40
50
60
70
80
90
100
Matisse Co-Clustering CLICK Random
GO-Process
GO-Compartment
MIPS Phenotypes
KEGG Pathways
% of modules with category enrichment at p< 10-3
0
5
10
15
20
25
30
35
40
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Matisse Co-Clustering CLICK Random
GO-Process
GO-Compartment
MIPS Phenotypes
KEGG Pathways
% annotations enriched at p<10-3 in modules
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GO and promoter analysisSubnetwork Size Front Enriched GO terms P-value TFs P-Value
1 120 119 processing of 20S pre-rRNA < 0.001 Fhl1 4.82E-16rRNA processing < 0.001 Rap1 2.89E-1135S primary transcript processing < 0.001 Sfp1 2.98E-08ribosomal large subunit assembly and maintenance 0.019rRNA modification < 0.001ribosome biogenesis 0.029
2 120 118 translational elongation < 0.001 Fhl1 1.03E-053 120 118 processing of 20S pre-rRNA < 0.001
rRNA processing 0.0335S primary transcript processing 0.011ribosomal large subunit assembly and maintenance 0.019ribosomal large subunit biogenesis < 0.001
5 120 112 signal transduction during filamentous growth 0.01 Ste12 5.41E-13conjugation with cellular fusion < 0.001 Dig1 5.41E-13
6 120 99 transcription from RNA polymerase III promoter < 0.001transcription from RNA polymerase I promoter 0.006
7 120 107 ergosterol biosynthesis < 0.001hexose transport 0.019
8 114 85 chromatin remodeling 0.0511 120 114 pseudohyphal growth 0.01 Msn2 3.17E-04
response to stress < 0.001 Msn4 1.82E-1214 120 102 ubiquitin-dependent protein catabolism 0.04715 120 96 nuclear mRNA splicing, via spliceosome < 0.00116 89 61 ubiquitin-dependent protein catabolism < 0.001 Rpn4 6.44E-0617 120 109 response to stress < 0.001 Msn4 1.74E-03
mitochondrial electron transport < 0.00118 87 59 nuclear mRNA splicing, via spliceosome 0.01220 46 35 pyridoxine metabolism 0.045 29
Application to stem cells• ~150 human stem cell lines of diverse
types profiled using microarrays• Clustered profiles into groups• Adjusted Matisse to seek subnetworks
that characteristic to each group • Focused analysis on pluripotent stem
cells
F. Müller, L. Laurent, D. Kostka, I. Ulitsky, R. Williams, C. Lu, I. Park, M. Rao, P. Schwartz, N. Schmidt, J. Loring Nature 08
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Pluripotent stem cells network
Highlights the key protein machinery underlying pluripotency
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Accounting for PPI confidence• PPI-based analysis is made difficult by
abundant false positive / negative interactions• Various methods can assign confidence
(probability) to individual edges• Idea: seek modules that are connected with
high probability
Ulitsky & Shamir Bioinformatics, 2009
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CEZANNE: (Co-Expression Zone ANalysis using NEtworks)
•Edge probability p(e) Edge weight –log(1-p(e))
•For any WU, ≥1 edge connects W with U\W with probability q (e.g. 0.95) The weight of the minimum cut of U is at least -log(1-q)
•Algorithm: among the subnets whose minimum cut exceeds -log(1-q) find the one with the maximum co-expression score
P({A},{B,C,D})=1-0.3*0.3=0.91
P({A,C,D},{B})=0.94P({A,B},{C,D})=0.94
P({A,B,D},{C})=0.994
minimum cut 0.7
0.9
0.70.8
A
B
C
D
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DNA damage response in S. cerevisiae• 47 DNA Damage Response
expression profiles (Gasch et al., 01)
• Front nodes: 2,074 genes with at least two-fold expression change
• Network and confidence values: purification enrichment (PE) scores (Collins et al. 07)
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Module size GO biological process p-value GO-slim protein complexes p-value
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ribosome biogenesis and assembly 1.2·10-117 ribosome 5.9·10-91
translation 1.0·10-85 eukaryotic 43S preinitiation complex 3.8·10-49
rRNA processing 7.5·10-79 small nucleolar ribonucleoprotein complex 1.5·10-41
35S primary transcript processing 4.6·10-44 DNA-directed RNA polymerase III complex 3.1·10-17
ribosome assembly 4.3·10-39 exosome (RNase complex) 4.4·10-15
ribosomal large subunit biogenesis 9.2·10-14 DNA-directed RNA polymerase I complex 5.7·10-14
rRNA modification 4.4·10-12 Noc complex 3.2·10-6
38protein catabolism 1.8·10-46 proteasome complex (sensu Eukaryota) 5.7·10-71
proteolysis 9.0·10-44 proteasome core complex (sensu Eukaryota) 9.4·10-32
ubiquitin cycle 1.1·10-42
12histone acetylation 3.6·10-13 histone acetyltransferase complex 2.1·10-12
chromatin modification 5.9·10-11
transcription from RNA polymerase II promoter 1.4·10-6
12 translation 1.1·10-14 ribosome 1.4·10-15
12nuclear mRNA splicing, via spliceosome 3.5·10-21 spliceosome complex 3.5·10-17
small nuclear ribonucleoprotein complex 2.5·10-15
10barbed-end actin filament capping 4.8·10-6 F-actin capping protein complex 4.8·10-6
endocytosis 1.1·10-5
cytoskeleton organization and biogenesis 2.8·10-5
8 establishment and/or maintenance of chromatin architecture 1.1·10-5 chromatin remodeling complex 4.6·10-6
7 glycogen metabolism 3.0·10-8 protein phosphatase type 1 complex 3.3·10-5
sporulation (sensu Fungi) 2.0·10-6
6 translation 1.1·10-7 ribosome 4.0·10-8
6 tRNA processing 2.5·10-14 ribonuclease P complex 9.2·10-8
rRNA processing 2.2·10-9
4 trehalose biosynthesis 6.8·10-14 alpha,alpha-trehalose-phosphate synthase complex (UDP-forming) 6.8·10-14
4 ubiquitin-dependent protein catabolism 5.2·10-7
3 pseudohyphal growth 9.8·10-7 cAMP-dependent protein kinase complex 9.6·10-7
3 proteasome assembly 3.2·10-6
protein folding 3.9·10-6
DNA damage response modules
Cytoplasmic ribosome biogenesis
Proteasome
Mitochondrial ribosome – small subunit
Mitochondrial ribosome – large subunit
Spliceosome
Novel actin-localized pathway?
Hsp90
PKA
Trehalose biosynthesis
Ribonuclease P
Suggests SWS2 a novel member
Novel pathway enriched with actin-localized proteins; Supported in other datasets; Similar
deletion phenotypes
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Comparison with prior work
Combined measure of sensitivity
(% of annotations enriched)
and specificity (% of modules enriched) with
p<0.001
Clustering of only expression data
Clustering expression &
network (Hanisch et al., 2002)
Expression similarity +
network connectivity
Expression similarity + confident network
connectivity
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Summary•Algorithms using co-expression + networks to
detect functionally coherent modules •Accommodate both sparse and dense
subnetworks•Subnetworks linked to osmotic shock and
DNA damage•A general framework for confident
connectivity in PPI networks•The next steps:
▫Co-expression is not the only interesting way to utilize GE data
▫Scaling to complex human datasets
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